Asian Pacific Journal of Cancer Prevention

Asian Pacific Journal of Cancer Prevention (아시아태평양암예방학회)
권호 표지
DOI QR코드

DOI QR Code

Partial Least Squares Based Gene Expression Analysis in EBV-Positive and EBV-Negative Posttransplant Lymphoproliferative Disorders

  • Wu, Sa (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Zhang, Xin (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Li, Zhi-Ming (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Shi, Yan-Xia (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Huang, Jia-Jia (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Xia, Yi (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Yang, Hang (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine) ;
  • Jiang, Wen-Qi (Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine)
  • Published : 2013.11.30
Download PDF
⟨ Previous Next ⟩

Abstract

Post-transplant lymphoproliferative disorder (PTLD) is a common complication of therapeutic immunosuppression after organ transplantation. Gene expression profile facilitates the identification of biological difference between Epstein-Barr virus (EBV) positive and negative PTLDs. Previous studies mainly implemented variance/regression analysis without considering unaccounted array specific factors. The aim of this study is to investigate the gene expression difference between EBV positive and negative PTLDs through partial least squares (PLS) based analysis. With a microarray data set from the Gene Expression Omnibus database, we performed PLS based analysis. We acquired 1188 differentially expressed genes. Pathway and Gene Ontology enrichment analysis identified significantly over-representation of dysregulated genes in immune response and cancer related biological processes. Network analysis identified three hub genes with degrees higher than 15, including CREBBP, ATXN1, and PML. Proteins encoded by CREBBP and PML have been reported to be interact with EBV before. Our findings shed light on expression distinction of EBV positive and negative PTLDs with the hope to offer theoretical support for future therapeutic study.

Keywords

References

  1. Adamson AL, Kenney S (1999). The Epstein-Barr virus BZLF1 protein interacts physically and functionally with the histone acetylase CREB-binding protein. J Virol, 73, 6551-8.
  2. Ashburner M, Ball CA, Blake JA, et al (2000). Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet, 25, 25-9. https://doi.org/10.1038/75556
  3. Auer RL, Dighiero G, Goldin LR, et al (2007). Trinucleotide repeat dynamic mutation identifying susceptibility in familial and sporadic chronic lymphocytic leukaemia. Br J Haematol, 136, 73-9. https://doi.org/10.1111/j.1365-2141.2006.06388.x
  4. Chakraborty S, Datta S, Datta S (2012). Surrogate variable analysis using partial least squares (SVA-PLS) in gene expression studies. Bioinformatics, 28, 799-806. https://doi.org/10.1093/bioinformatics/bts022
  5. Craig FE, Johnson LR, Harvey SA, et al (2007). Gene expression profiling of Epstein-Barr virus-positive and -negative monomorphic B-cell posttransplant lymphoproliferative disorders. Diagn Mol Pathol, 16, 158-68. https://doi.org/10.1097/PDM.0b013e31804f54a9
  6. Gosselin. R, Rodrigue. D, Duchesne C (2010). A Bootstrap-VIP approach for selecting wavelength intervals in spectral imaging applications. Chemometrics and Intelligent Laboratory Systems, 100, 12-21. https://doi.org/10.1016/j.chemolab.2009.09.005
  7. Gottschalk S, Rooney CM, Heslop HE (2005). Post-transplant lymphoproliferative disorders. Annu Rev Med, 56, 29-44. https://doi.org/10.1146/annurev.med.56.082103.104727
  8. Irizarry RA, Hobbs B, Collin F, et al (2003). Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics, 4, 249-64. https://doi.org/10.1093/biostatistics/4.2.249
  9. Ji G, Yang Z, You W (2011). PLS-Based Gene Selection and Identification of Tumor-Specific Genes. Ieee Transactions On Systems, Man, And Cybernetics-Part C: Applications And Reviews, 41, 830-41. https://doi.org/10.1109/TSMCC.2010.2078503
  10. Kanehisa M, Goto S (2000). KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res, 28, 27-30. https://doi.org/10.1093/nar/28.1.27
  11. Martins JPA, Teofilo RF, Ferreira MMC (2010). Computational performance and cross-validation error precision of five PLS algorithms using designed and real data sets. Journal of Chemometrics, 24, 320-32.
  12. Morscio J, Dierickx D, Ferreiro JF, et al (2013). Gene expression profiling reveals clear differences between EBV-positive and EBV-negative posttransplant lymphoproliferative disorders. Am J Transplant, 13, 1305-16. https://doi.org/10.1111/ajt.12196
  13. Nelson BP, Nalesnik MA, Bahler DW, et al (2000). Epstein-Barr virus-negative post-transplant lymphoproliferative disorders: a distinct entity? Am J Surg Pathol, 24, 375-85. https://doi.org/10.1097/00000478-200003000-00006
  14. Nicewonger J, Suck G, Bloch D, Swaminathan S (2004). Epstein-Barr virus (EBV) SM protein induces and recruits cellular Sp110b to stabilize mRNAs and enhance EBV lytic gene expression. J Virol, 78, 9412-22. https://doi.org/10.1128/JVI.78.17.9412-9422.2004
  15. Paya CV, Fung JJ, Nalesnik MA, et al (1999). Epstein-Barr virus-induced posttransplant lymphoproliferative disorders. ASTS/ASTP EBV-PTLD Task Force and The Mayo Clinic Organized International Consensus Development Meeting. Transplantation, 68, 1517-25. https://doi.org/10.1097/00007890-199911270-00015
  16. Shannon P, Markiel A, Ozier O, et al (2003). Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res, 13, 2498-504. https://doi.org/10.1101/gr.1239303
  17. Shi M, Gan YJ, Davis TO, Scott RS (2013). Downregulation of the polyamine regulator spermidine/spermine N-acetyltransferase by Epstein-Barr virus in a Burkitt's lymphoma cell line. Virus Res.
  18. Stelzl U, Worm U, Lalowski M, et al (2005). A human protein-protein interaction network: a resource for annotating the proteome. Cell, 122, 957-68. https://doi.org/10.1016/j.cell.2005.08.029
  19. Wang L, Grossman SR, Kieff E (2000). Epstein-Barr virus nuclear protein 2 interacts with p300, CBP, and PCAF histone acetyltransferases in activation of the LMP1 promoter. Proc Natl Acad Sci U S A, 97, 430-5. https://doi.org/10.1073/pnas.97.1.430

Cited by

  1. Promyelocytic Leukemia Gene Functions and Roles in Tumorigenesis vol.15, pp.19, 2014, https://doi.org/10.7314/APJCP.2014.15.19.8019
  2. Pathway and Network Analysis in Glioma with the Partial Least Squares Method vol.15, pp.7, 2014, https://doi.org/10.7314/APJCP.2014.15.7.3145